OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 7 — Mar. 1, 2012
  • pp: B49–B56

Robust validation of pattern classification methods for laser-induced breakdown spectroscopy

Jeremiah Remus and Kehinde S. Dunsin  »View Author Affiliations


Applied Optics, Vol. 51, Issue 7, pp. B49-B56 (2012)
http://dx.doi.org/10.1364/AO.51.000B49


View Full Text Article

Enhanced HTML    Acrobat PDF (360 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Laser-induced breakdown spectroscopy (LIBS) is an emerging technology that is suitable for a variety of material identification applications. For LIBS to successfully transition from the laboratory into field applications, the sensor must be paired with the appropriate algorithms for accurate and robust processing of the LIBS spectra. In this study we will report on the results of testing classification methods on eight distinct classification tasks using LIBS datasets. Results suggest that standard cross-validation techniques may not accurately estimate generalization performance and a proposed “leave-one-sample-out” approach to experiment design for classifier validation may provide a more robust measure of performance.

© 2012 Optical Society of America

OCIS Codes
(170.1580) Medical optics and biotechnology : Chemometrics
(300.6360) Spectroscopy : Spectroscopy, laser
(300.6365) Spectroscopy : Spectroscopy, laser induced breakdown

History
Original Manuscript: October 3, 2011
Manuscript Accepted: November 4, 2011
Published: February 9, 2012

Citation
Jeremiah Remus and Kehinde S. Dunsin, "Robust validation of pattern classification methods for laser-induced breakdown spectroscopy," Appl. Opt. 51, B49-B56 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-7-B49


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. R. Noll, I. Mönch, O. Klein, and A. Lamott, “Concept and operating performance of inspection machines for industrial use based on laser-induced breakdown spectroscopy,” Spectrochim. Acta, Part B: At. Spectrosc. 60, 1070–1075 (2005).
  2. F. R. Doucet, T. F. Belliveau, J. L. Fortier, and J. Hubert, “Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminum alloys,” Appl. Spectrosc. 61, 327–332 (2007). [CrossRef]
  3. R. S. Harmon, J. Remus, N. J. McMillan, C. McManus, L. Collins, J. L. Gottfried, F. C. DeLucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24, 1125–1141 (2009). [CrossRef]
  4. R. C. Chinni, D. A. Cremers, L. J. Radziemski, M. Bostian, and C. Navarro-Northrup, “Detection of uranium using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 63, 1238–1250 (2009). [CrossRef]
  5. E. C. Ferreira, D. M. B. P. Milori, E. J. Ferreira, R. M. Silva Da, and L. Martin-Neto, “Artificial neural network for Cu quantitative determination in soil using a portable Laser Induced Breakdown Spectroscopy system,” Spectrochim. Acta, Part B: At. Spectrosc. 63, 1216–1220 (2008).
  6. A. Ramil, A. López, and A. Yáñez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS),” Appl. Phys. A 92, 197–202 (2008). [CrossRef]
  7. G. Zadora, “Glass analysis for forensic purposes—a comparison of classification methods,” J. Chemom. 21, 174–186 (2007).
  8. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1995), Vol. 2, pp. 1137–1145.
  9. S. de Jong, “SIMPLS: An alternative approach to partial least squares regression,” Chemometr. Intell. Lab. Syst. 18, 251–263 (1993).
  10. B. Matthew and R. William, “Partial least squares for discrimination,” J. Chemom. 17, 166–173 (2003). [CrossRef]
  11. L. Breiman, “Random Forests,” Mach. Learn. 45, 5–32 (2001). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited